Kalman Filter Based Controlled Online System Identification | Journal of Engineering Sciences

Kalman Filter Based Controlled Online System Identification

Author(s): Ganesh E. N.

Affilation(s): Saveetha Engineering College, Kuthambakkam, 600124 Tamil Nadu, Chennai, India

*Corresponding Author’s Address: enganesh50@gmail.com

Issue: Volume 5; Issue 2 (2018)

Paper received: July 13, 2018
The final version of the paper received: September 24, 2018
Paper accepted online: October 3, 2018

Ganesh E. N. Kalman Filter Based Controlled Online System Identification / E. N. Ganesh // Journal of Engineering Sciences. – Sumy : Sumy State University, 2018. – Volume 5, Issue 2. – P. E22-E26.

DOI: 10.21272/jes.2018.5(2).e5

Research Area: MECHANICAL ENGINEERING: Computational Mechanics

Abstract. In the development of model predictive controllers a significant amount of time and effort is necessary for the development of the empirical control models. Even if on-line measurements are available, the control models have to be estimated carefully. The payback time of a model predictive controller could be significantly reduced, if a common identification tool would be available which could be introduced in a control scheme right away. In this work it was developed a control system which consists of a neural network (NN) with external recurrence only, whose parameters are adjusted by the extended Kalman filter in real-time. The output of the neural network is used in a control loop to study its accuracy in a control loop. At the moment this control loop is a NN-model based minimum variance controller. The on-line system identification with controller was tested on a simulation of a fed-batch penicillin production process to understand its behaviour in a complex environment. On every signal process and measurements noise was applied. Even though the NN was never trained before, the controller did not diverge. Although it seemed like the on-line prediction of the NN was quite accurate, the real process was not learned yet. This was checked by simulating the process with the NN obtained at the end of the batch. Nevertheless the process was maintained under control near the wanted set-points. These results show a promising start for a model predictive controller using an on-line system identification method, which could greatly reduce implementation times.

Keywords: Kalman filter, neural network, on-line training, variance control.


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